Kaisar Kushibar

FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare

2025 · 289 Zit. · BMJ

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

2021 · 142 Zit. · Physica Medica

Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging

2022 · 76 Zit. · Medical Image Analysis

medigan: a Python library of pretrained generative models for medical image synthesis

2023 · 43 Zit. · Journal of Medical Imaging

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

2023 · 38 Zit. · arXiv (Cornell University)

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

2021 · 12 Zit. · arXiv (Cornell University)

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions.

2021 · 12 Zit. · Spiral (Imperial College London)

Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease

2021 · 8 Zit. · Research Square

Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge

2025 · 8 Zit. · Nature Communications

Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging

2021 · 3 Zit. · arXiv (Cornell University)

A Review of Methods for Trustworthy AI in Medical Imaging: The FUTURE-AI Guidelines

2025 · 3 Zit. · IEEE Journal of Biomedical and Health Informatics

Fairness-Aware Data Augmentation for Cardiac MRI Using Text-Conditioned Diffusion Models

2025 · 1 Zit. · Lecture notes in computer science

Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease

2021 · 1 Zit. · arXiv (Cornell University)

Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results

2025 · 1 Zit. · Computers in Biology and Medicine

A review of methods for trustworthy AI in medical imaging: The FUTURE-AI Guidelines

2025 · 0 Zit. · Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona)